Practical machine learning and image processing : for facial recognition, object detection, and pattern recognition using Python

Responsibility
Himanshu Singh.
Digital
text file; PDF
Publication
[Berkeley, California] : Apress, [2019]
Physical description
1 online resource

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Description

Creators/Contributors

Author/Creator
Singh, Himanshu, author.

Contents/Summary

Contents
  • Chapter 1: Installation and Environment Setup
  • Chapter Goal: Making System Ready for Image Processing and Analysis
  • No of pages 20
  • Sub -Topics (Top 2)
  • 1.
  • Installing Jupyter Notebook
  • 2.
  • Installing OpenCV and other Image Analysis dependencies
  • 3.
  • Installing Neural Network Dependencies
  • Chapter 2: Introduction to Python and Image Processing
  • Chapter Goal: Introduction to different concepts of Python and Image processing Application on it.
  • No of pages: 50
  • Sub - Topics (Top 2)
  • 1. Essentials of Python
  • 2. Terminologies related to Image Analysis
  • Chapter 3: Advanced Image Processing using OpenCV
  • Chapter Goal: Understanding Algorithms and their applications using Python
  • No of pages: 100
  • Sub - Topics (Top 2):
  • 1.
  • Operations on Images
  • 2.
  • Image Transformations
  • Chapter 4: Machine Learning Approaches in Image Processing
  • Chapter Goal: Basic Implementation of Machine and Deep Learning Models, which takes care of Image Processing, before applications in real-time scenario
  • No of pages: 100
  • Sub - Topics (Top 2):
  • 1.
  • Image Classification and Segmentation
  • 2.
  • Applying Supervised and Unsupervised Learning approaches on Images using Python
  • Chapter 5: Real Time Use Cases
  • Chapter Goal: Working on 5 projects using Python, applying all the concepts learned in this book
  • No of pages: 100
  • Sub - Topics (Top 5):
  • 1. Facial Detection
  • 2. Facial Recognition
  • 3. Hand Gesture Movement Recognition
  • 4. Self-Driving Cars Conceptualization: Advanced Lane Finding
  • 5. Self-Driving Cars Conceptualization: Traffic Signs Detection
  • Chapter 6: Appendix A
  • Chapter Goal: Advanced concepts Introduction
  • No of pages: 50
  • Sub - Topics (Top 2):
  • 1. AdaBoost and XGBoost
  • 2. Pulse Coupled Neural Networks.
  • (source: Nielsen Book Data)
Publisher's summary
Gain insights into image-processing methodologies and algorithms, using machine learning and neural networks in Python. This book begins with the environment setup, understanding basic image-processing terminology, and exploring Python concepts that will be useful for implementing the algorithms discussed in the book. You will then cover all the core image processing algorithms in detail before moving onto the biggest computer vision library: OpenCV. You'll see the OpenCV algorithms and how to use them for image processing. The next section looks at advanced machine learning and deep learning methods for image processing and classification. You'll work with concepts such as pulse coupled neural networks, AdaBoost, XG boost, and convolutional neural networks for image-specific applications. Later you'll explore how models are made in real time and then deployed using various DevOps tools. All the concepts in Practical Machine Learning and Image Processing are explained using real-life scenarios. After reading this book you will be able to apply image processing techniques and make machine learning models for customized application. What You Will Learn Discover image-processing algorithms and their applications using Python Explore image processing using the OpenCV library Use TensorFlow, scikit-learn, NumPy, and other libraries Work with machine learning and deep learning algorithms for image processing Apply image-processing techniques to five real-time projects Who This Book Is For Data scientists and software developers interested in image processing and computer vision.
(source: Nielsen Book Data)

Bibliographic information

Publication date
2019
In
Springer eBooks
ISBN
9781484241493 (electronic bk.)
1484241495 (electronic bk.)
9781484241509 (print)
1484241509
1484241487
9781484241486
DOI
10.1007/978-1-4842-4149-3

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